1. 9.913 Pattern Recognition for Vision Class9 - Object Detection and Recognition Bernd Heisele

2. Outline Object Detection Object Recognition

3. Object Detection Task: Given an input image, determine if there are objects of a given class in the image and where they are located.

4. Face Detection System Architecture

5. Testing

6. Image Features

7. ROC for Image Features Gray Gray + Haar Haar Gray + Grad

8. Positive Training Data

9. Real vs. Synthetic Real Synthetic

10. ROC for Classifiers LDA Linear SVM Poly2

11. Global vs. Components (Whole Face)

12. Component-based Detection

13. Some Examples

14. ROC Component vs. Global About 40000 faces 68 people 13 poses 43 illuminations condition CMU PIE database

15. Training on Faces Positive Facial Negative Non-facial Negative Use the remainder of the face in the negative training set

16. Training on Faces Red: Trained on facial and non-facial negative set. Blue: Trained only on non-facial negative set.

17. Pair-wise Biasing Often, many components classify correctly, with only a few errors. Use the pair-wise relative position information from training data to bias the result image.

18. Pair-wise Biasing Result Images Biased Results

19. ROC Pair-wise Biasing Red: Trained on facial and non-facial negative set. Blue: Trained only on non-facial negative set. Dashed: Biasing and trained on facial and non-facial negative set.

20. Pedestrian Detection

21. Object Recognition Task: Given an image of and object of a particular class identify which exemplar it is.

22. Recognition System Architecture

23. Multi-class Classification with SVM Training: N (N-1) / 2 Classification: N - 1 Training: N Classification: N The two different architecture has similar performance!!

24. Global Approach 1. Detect and extract face 2. Feed gray values of extracted face into N SVMs 3. Classify based on maximum output Each SVM is one vs. all approach

25. Global Approach with Clustering T1. Partition training images of each person into viewpoint- specific clusters T2. Train a SVM on each cluster. R1. Detect and extract face R2. Feed extracted face to all SVMs R3. Take maximum over all SVM outputs

26. Component-based Approach 1. Detect face and extract components 2. Combine gray values of components to a feature vector, and feed to the N SVMs 3. Take maximum over all SVM outputs

27. ROC Component vs. Global Recognition Trained and tested on frontal and rotated faces.